Luciano Pietronero

Institute of Complex Systems

Luciano Pietronero studied physics in Rome and was a research scientist at Xerox Research in Webster (1974) and Brown Boveri Research Center (CH) 1975-1983. He then moved to Univ. of Groningen (NL), where he was professor of Condensed Matter Theory (1983-87). Since 1987 he is professor of Physics at the University of Rome "Sapienza. Founder and director of the Institute for Complex Systems of CNR (2004-2014). Broad international experience in academic and industrial enviroments. The scientific activity is of both fundamental and applied nature, with a problem oriented interdisciplinary perspective. Development of novel and original views in all the areas of activity. Leader of a generation of joung scientists who are protagonists of the complexity scene internationally.
In 2008 he received the Fermi Prize (highest award of the Italian Physical Society).
Research interests Condensed Matter Theory; High-temperature superconductivity; Statistical Physics; Fractal Growth; Self-Organized-Criticality; Complex Systems and its interdisciplinary applications. Recent activity in Economic Complexity:
http://lucianopietronero.it

Speeches di Luciano Pietronero

Big Data: Opportunity and Mith

Economic Fitness and Complexity represent a new field of research that consists in a radically new methodology. It describes economics as evolutionary process of ecosystems made of industrial and financial technologies that are all globally interconnected. This offers new opportunities to constructively describe technological ecosystems, analyse their structures, understand their internal dynamics, as well as to introduce new metrics. This approach provides a new paradigm for a fundamental economic science based on data and not on ideologies or interpretations. One characteristics is to go from the many parameters of the standard economic analysis to a new methodology with zero parameters. This dimensional reduction is essential for a novel approach to the analysis and forecasting beyond the standard regressions [1, 2].
A crucial element of our methodology is a radically new approach to the problem of Big Data. Big Data is often associated with "big noise" as well as a subjective ambiguity related to how to structure the data and how to assign them a value that should reflect many arbitrary parameters. In the case of the evaluation of the industrial competitiveness of a country, the required parameters for such an analysis could more than one hundred. A key point approach EC is to go from 100 parameters to zero parameters and obtain results which can be tested in a scientific perspective. This is done by focusing on the data in which the signal to noise ratio is optimal and developing iterative algorithms in the spirit, but other than Google, and optimized to the economic problem in question. In particular the study of a country or a company is not done at the individual level but through the global network in which it is inserted. In this way you get the Fitness of the countries and the Complexity of the products.
In a collaboration with IFC-World Bank we have presented a detailed comparison of the GDP forecasting based on the Fitness methodology with the standard IMF forecasting. According to a recent report by Bloomberg Views: The new Fitness method systematically outperforms standard methods, despite requiring much less data [3].
References
[1] A. Tacchella, M. Cristelli, G. Caldarelli, A. Gabrielli and L. Pietronero: A New Metrics for Countries Fitness and Products Complexity, Nature: Scientific Reports, 2-723 (2012); M. Cristelli, A. Gabrielli, A. Tacchella, G. Caldarelli and L. Pietronero: Measuring the Intangibles: A Metrics for the Economic Complexity of Countries and Products, PLOS One Vol. 8, e70726 (2013)
[2] M. Cristelli, A. Tacchella, L. Pietronero: The Heterogeneous Dynamics of Economic Complexity, PLOS One 10(2): e0117174 (2015) and Nature editorial 2015: http://www.nature.com/news/physicists-make-weather-forecasts-for-economies-1.16963
[3] https://www.bloomberg.com/view/articles/2017-10-01/a-better-way-to-make-economic-forecasts